Journal cover Journal topic
Proceedings of the ICA
Journal topic
Articles | Volume 4
Proc. Int. Cartogr. Assoc., 4, 37, 2021
https://doi.org/10.5194/ica-proc-4-37-2021
Proc. Int. Cartogr. Assoc., 4, 37, 2021
https://doi.org/10.5194/ica-proc-4-37-2021

  03 Dec 2021

03 Dec 2021

COVID-19 geoviz for spatio-temporal structures detection

Jacques Gautier, Maria-Jesus Lobo, Benjamin Fau, Armand Drugeon, Sidonie Christophe, and Guillaume Touya Jacques Gautier et al.
  • LASTIG, Univ Gustave Eiffel, ENSG, IGN, F-94160 Saint-Mande, France

Keywords: spatio-temporal visualization, epidemic visualization, spatio-temporal structures, exploratory analysis, 3D visualization

Abstract. The spread of COVID-19 has motivated a wide interest in visualization tools to represent the pandemic’s spatio-temporal evolution. This tools usually rely on dashboard environments which depict COVID-19 data as temporal series related to different indicators (number of cases, deaths) calculated for several spatial entities at different scales (countries or regions). In these tools, diagrams (line charts or histograms) display the temporal component of data, and 2D cartographic representations display the spatial distribution of data at one moment in time. In this paper, we aim at proposing novel visualization designs in order to help medical experts to detect spatio-temporal structures such as clusters of cases and spatial axes of propagation of the epidemic, through a visual analysis of detailed COVID-19 event data. In this context, we investigate and revisit two visualizations, one based on the Growth Ring Map technique and the other based on the space-time cube applied on a spatial hexagonal grid. We assess the potential of these visualizations for the visual analysis of COVID-19 event data, through two proofs of concept using synthetic cases data and web-based prototypes. The Grow Ring Map visualization appears to facilitate the identification of clusters and propagation axes in the cases distribution, while the space-time cube appears to be suited for the identification of local temporal trends.

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